VectorBT

Fetch historical price data for Bitcoin using `vbt.YFData.download`.

Calculate 10-day and 20-day moving averages using `vbt.MA.run`.

Generate entry signals when the 10-day MA crosses above the 20-day MA using `fast_ma.ma_crossed_above(slow_ma)`.

Generate exit signals when the 10-day MA crosses below the 20-day MA using `fast_ma.ma_crossed_below(slow_ma)`.

Backtest the strategy using `vbt.Portfolio.from_signals` and analyze the total return.

Define a range of fast and slow moving average windows (e.g., 10-20 days for fast and 20-30 days for slow).

Calculate moving averages for each window size using `vbt.MA.run`.

Generate entry and exit signals for each combination of window sizes.

Backtest the strategy for each combination using `vbt.Portfolio.from_signals`.

Compare the total returns to identify the optimal window sizes.

Fetch historical price data for Bitcoin and Ethereum using `vbt.YFData.download`.

Combine the price series into a single DataFrame using `btc_price.vbt.concat(eth_price)`.

Calculate moving averages for the combined price data.

Generate entry and exit signals.

Backtest the strategy using `vbt.Portfolio.from_signals` and compare the returns for each asset.

Fetch a large dataset of historical price data for multiple assets.

Apply feature engineering techniques to create new features (e.g., volatility, momentum).

Use VectorBT to backtest strategies based on these features.

Analyze the results to identify patterns that consistently lead to profitable trades.

Develop a trading strategy based on the identified patterns.

Define a custom technical indicator function.

Apply the indicator function to historical price data.

Generate entry and exit signals based on the indicator's values.

Backtest the strategy using `vbt.Portfolio.from_signals`.

Analyze the results to determine the indicator's effectiveness.